超越疾病孤岛的思考:由系统生物学和机器学习识别的结核病和肺癌中常见的失调基因
1Department of Biotechnology, Brainware University, Barasat, India.
Omics : a journal of integrative biology
|June 10, 2024
概括
这项研究揭示了结核病 (TB) 和肺癌 (LC) 之间的共同分子基础,表明了新的诊断和治疗途径. 生物信息学分析发现了重叠的基因和途径,强调了重新考虑疾病分类的必要性.
科学领域:
- 生物信息学和计算生物学
- 基因组学和分子生物学
- 一个健康和行星健康
背景情况:
- 传统的疾病分类往往忽略了共同的分子机制.
- 结核病 (TB) 和肺癌 (LC) 是不同的肺部疾病,具有显著的全球健康影响.
- 这些疾病可以同时发生,为医学创新带来挑战和机遇.
研究的目的:
- 用生物信息学来识别TB和LC之间共享的分子基质.
- 基于重叠的基因表达来探索潜在的诊断和治疗点.
- 在疾病研究中倡导一个健康和行星健康的观点.
主要方法:
- 对公开可用的基因表达数据集的分析 (NCBI-GEO: GSE83456, GSE103888).
- 鉴定差异表达的基因 (例如,CEACAM6,MUC1,GAS6) 和相关途径 (氨酸降解).
- 机器学习的应用 (随机森林) 用于疾病分类和分子对接用于药物标验证.
主要成果:
- 在结核病和LC中发现了重叠的失调基因 (CEACAM6,MUC1,ADM,DYSF,PLOD2,GAS6).
- 途径分析将这些基因与氨酸降解途径联系起来.
- 机器学习模型在使用这些基因来区分疾病与对照者方面取得了很高的准确性 (84%用于结核病,83%用于LC).
- 分子对接证实了华法林与GAS6的结合亲和力,表明了潜在的治疗点.
结论:
- 结核病和LC可能有共同的分子路径,挑战传统的疾病类别.
- 已识别的基因和途径为诊断提供了潜在的生物标志物和治疗的目标.
- 采用One Health和行星健康框架可以促进了解和管理复杂疾病的创新方法.
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